The ARC Prize organization designs benchmarks which are specifically crafted to demonstrate tasks that humans complete easily, but are difficult for AIs like LLMs, “Reasoning” models, and Agentic frameworks.
ARC-AGI-3 is the first fully interactive benchmark in the ARC-AGI series. ARC-AGI-3 represents hundreds of original turn-based environments, each handcrafted by a team of human game designers. There are no instructions, no rules, and no stated goals. To succeed, an AI agent must explore each environment on its own, figure out how it works, discover what winning looks like, and carry what it learns forward across increasingly difficult levels.
Previous ARC-AGI benchmarks predicted and tracked major AI breakthroughs, from reasoning models to coding agents. ARC-AGI-3 points to what’s next: the gap between AI that can follow instructions and AI that can genuinely explore, learn, and adapt in unfamiliar situations.
You can try the tasks yourself here: https://arcprize.org/arc-agi/3
Here is the current leaderboard for ARC-AGI 3, using state of the art models
- OpenAI GPT-5.4 High - 0.3% success rate at $5.2K
- Google Gemini 3.1 Pro - 0.2% success rate at $2.2K
- Anthropic Opus 4.6 Max - 0.2% success rate at $8.9K
- xAI Grok 4.20 Reasoning - 0.0% success rate $3.8K.
(Logarithmic cost on the horizontal axis. Note that the vertical scale goes from 0% to 3% in this graph. If human scores were included, they would be at 100%, at the cost of approximately $250.)
https://arcprize.org/leaderboard
Technical report: https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf
In order for an environment to be included in ARC-AGI-3, it needs to pass the minimum “easy for humans” threshold. Each environment was attempted by 10 people. Only environments that could be fully solved by at least two human participants (independently) were considered for inclusion in the public, semi-private and fully-private sets. Many environments were solved by six or more people. As a reminder, an environment is considered solved only if the test taker was able to complete all levels, upon seeing the environment for the very first time. As such, all ARC-AGI-3 environments are verified to be 100% solvable by humans with no prior task-specific training
Tell me again how AGI is just around the corner, Sam
When Sammy fuck says “we’re so close to AGI, I can just feel it. Like a tingle on the tip of my shrimpdick it’s getting so close to blossoming into something guys”, just ignore him. He’s crazy man!
"Like a tingle on the tip of my shrimpdick it’s getting so close to blossoming into something guys”
Wow, that really is something. XD
I’m trying to stretch my creative muscles more XD
a tingle on the tip of my shrimpdick
mhh that’s erotic ASMR on Youtube
to be fair, he’s not human so he’s just guessing based on his observations earth as a demon
machines will be able to ‘think like humans’ when it happens
Maybe AGI is just a brain-destroying pandemic?
just when he had to shut down sora, because making ai videos is too expensive.
I know lemmy’s very anti-ai but this is really fascinating stuff.
We’re anti-AI because AI is fucking stupid. Both literally and figuratively.
It really isn’t. But you do you boo.
Someone else in the comments said it perfectly. AI is just data regurgitation. It’s like calling me highly intelligent because I read you a paragraph from Wikipedia. I didn’t know anything. I just read a thing and said it out loud.
No. You’re not just wrong, you’re aggressively uninformed.
By you repeating the same tired “AI is just regurgitating data” line makes it clear you don’t understand what you’re criticizing. Calling large language models “AI” the way you are doing it just exposes that you do not know what you are talking about. It is like a creationist smugly saying “orangutang” instead of “orangutan” and thinking they sound informed. You are not demonstrating insight. You are advertising ignorance.
What you’re describing, reading a paragraph off Wikipedia, is literal retrieval. That is not how modern language models operate. They are not databases with a search bar attached. They are probabilistic systems trained to model patterns, structure, and relationships across massive datasets. When they generate a response, they are not pulling a stored paragraph. They are constructing output token by token based on learned representations.
If it were just regurgitation, you would constantly see verbatim copies of training data. You do not. What you see instead is synthesis. Concepts are recombined, abstracted, and adapted to context. The system can explain the same idea multiple ways, shift tone, handle novel prompts, and connect ideas that were never explicitly paired in the source material. That is fundamentally different from reading something out loud.
Your analogy fails because it assumes nothing is being transformed. In reality, transformation is the entire mechanism. Information is compressed into weights and then expanded into new outputs.
Is it human intelligence. No. Is it perfect. No. But reducing it to “just reading Wikipedia out loud” is not skepticism. It is a basic failure to understand how the technology works.
If you are going to criticize something, at least learn what it is first.
Counterpoint: Why should they learn about it?
It is a good thing to reduce ignorance, but there is more to learn in the world than there is time to learn or space in the brain. People must specialise.
You must accept that not everyone will understand everything, and this is okay.
The nature of a Large Language Model is very specialist knowledge, data regurgitation is apt from a distance, especially when most publically available models are primarily used for search.
Criticism must be accepted, even from those who do not understand, so long as it’s in good faith. It is after all an opportunity to reduce ignorance to someone with the time and interest to learn.
Don’t rudely lord your intelligence over someone else, it might not end well, and invalidates the delivery of your entire argument.
The reason he should learn about it is because he’s talking about it as though he’s informed and he is not.
I don’t have to be a LLM programmer working at openai to have a working knowledge of how these machines function. It’s literally just a Google search.
He made an unreasonable ignorant comment and I called him out. He should feel ashamed and I have absolutely no reason to pad down what I’m saying under the guise of being nice.
This might be the most comprehensive comment I’ve ever read about someone saying how utterly stupid they are to the world. It’s incredibly impressive how articulate you described your absolute lack of critical thinking.
It’s almost like intentionally shooting yourself in the nuts, and openly releasing the video of it saying you promote gun safety.
Calling an llm a Wikipedia regurgitator is factually and objectively incorrect.
Is there anything that you can say to refute the facts that I presented in my above comment?
(I rolled my eye so hard at your comment that I pulled my back out)
You’re discounting the fact that a human reading Wikipedia will attribute intonation and tone to the text to give further context and meaning. I think the analogy is good. Its not precise but it is the same thing.
I do think AI has a useful purpose and is here to stay. I don’t think it’s groundbreaking like the AI companies want us to think. The bubble will burst and then we’ll see where the cards lie.
OpenAI has lost their lead and I expect they will start to struggle with further funding. There are quite a few warning signs. The price of oil is likely to increase power prices generally and cause construction delays and cost rises. Both will hamper their plans. They still don’t have a viable model for profit.
The analogy is terrible and is not at all, once again, what llms do.
This is an objective fact I have provided evidence to support this.
How are you saying the analogy is good?
Ana analogy does not need to be precise. It expresses a comparison for easier understanding. It is not what LLMs do. However what you’ve expressed is simplified also. So by your standard, it is not useful for the discussion.
So maybe get your head out of your ass and try to understand what people are trying to express instead of correcting them when they are not incorrect.
If precision was of that much importance to you, you would have a different opinion of LLMs.
I can’t see AI actually being intelligent until they no longer need to send a built up prompt of guides and skills and the chat history on every submission.
It’s no different from Alexa 15 years ago with skills. Just a better protocol and interface and ability to parse the current user prompt.
In my opinion of course.
Ya i agree. The whole infrastructure of how these work is flawed for a true AI/AGI.
It might be able to do a lot of cool things, but its fundamentally flawed at its core.
Someone will need to figure out something completely different for a true AI.
Oh also, I remember Elon once talked about how the upcoming cars would get bored when they weren’t doing anything with all that compute while parked so they could do use that compute and pay people for it.
Paying for the compute isnt a terrible idea in the future, but become bored? LOL. Fucking crazy talk.
Like even if it was a true AI that could be bored. You’re now going to enslave it to do what you want on its free time?
Yeah, if it’s got the capacity to be bored it’s not going to stick around waiting for you. Pets act out when bored, as will AI, better to let the ghost in the machine go have fun in an arcade or something.
Current models can pretend to be bored when directed to, but they’re only facsimiles of thought at the moment, and the current approach probably won’t change that.
Right? I have a Google Home Mini in our kitchen and if we ask it a question it just pulls a source from a website and tells us. That’s it. Nothing intelligent about it.
AI now is no different. It’s just pulling more complex wording from more (albeit illegally) sources to give a (albeit sometimes incorrect) better description of the question asked.
AI is just as stupid as Alexa is/was 15 years ago. It just has more information to pull from and still fucks it up.
LLM’s are just very well-read morons.
It’s almost as if a chatbot isn’t actually thinking.
I tend to be anti-AI because it doesn’t seem to me to be anything other than a super fast regurgitator of data. If a database can be searched for an answer, AI can do that faster than a human. However it doesn’t to seem to be able to take some portion of that database, understand it, and then use that information to solve a novel problem.
Well… It cannot even search databases without errors.
LLMs just produce plausible replies in natural languages very quickly and this is useful in certain situations. Sometimes it helps humans getting started with a task, but as it is now, it cannot replace them. As much as the capital class want it, and sink our money into it.
The better setup generate “semantic embeddings” that try to map how data stored relate to each other (by mapping how to it related within in its own weights and biases). That and knowledge graph look ups in which the links between different articles of data are evaluated in the same way.
The very expensive LLM portion really do just give rough aproximations of information language in that setup
Yes, the key thing is it might have extracted useful info from otherwise confusing data, it might have mixed up info from the data incorrectly or it might have just made it up.
So it can be useful, if you can then validate the info provided in more traditional means, but it’s dubious as a first pass, and sometimes surprisingly bad when it’s a scenario you thought it would work well at.
This replay is the funniest shit lmao. Keep building that bridge Claude.
https://arcprize.org/replay/0964128b-a2f5-4c5b-886e-497d893f429d
Interesting that it seems to be perceiving the environment mostly accurately, and is just completely wrong about the purpose of all the game objects.
I couldn’t find replays. Are there more? Also, it is a bit funny that “building the bridge” which at one point seems to be Claude’s “chosen goal” is just “running out of moves” and failing the task.
Task failed successfully, Claude. Task failed, successfully.
There’s a column linking to replays in the table of tasks here: https://arcprize.org/tasks
Here’s another reply where the model mistakes running out of time/move for making progress
it’s reasoning log is so fucking funny
My understanding is that Claude is particularly geared towards being a tool for people to use rather than a human replacement. That’s why they had that whole spat with the Pentagon about a human needing to be in the loop.
Try spelling things phonetically (example: faux net tick alley), that’s one of my benchmarks that AI fails almost every time.
If the input is at all long, or purposefully includes a lot of words about a specific unrelated theme to the coded message, it’s impossible.
Oh that’s an interesting challenge.
I hear some LLMs now have some solutions for the classic “how many Rs in ‘strawberry’” problem (related to the tokenization processes), but I have no idea how they might solve the phonetic thing. I’m sure some smart people will eventually find a way though
Wait, I thought phonetically (example: papa hotel oscar novermber echo tango india charle alfa lima lima yankee) meant using a phonetic alphabet, not using word(s) with the same Soundex encoding.
Yeah, there was some phonics in my primary school education, and I continue to approach new words in that way sometimes. But, they said Phonetically.
Phonetics is the study of speech sounds. The phonetic alphabet is called that because each letter/word in the alphabet was chosen to be one that started with the corresponding phoneme and that the set of words were between them phonetically unambiguous. Phonics is a way of teaching reading and writing that is based on the phonetics of words and how they relate to the written form.
They get 85% on the last benchmark, this one was specifically designed to stump them, when the last one came out everyone said the same things as this go around.
will anyone be retracting their statements when they get to 85% on this one?
So they already have AGI? Why doesn’t it solve new problems then? Bunch of bullshit, they’re just adjusting their models to the “benchmarks” to get more VC funding
They are not adjusting their models at any cost to other benchmarks. Especially when the benchmark isn’t even public. This would be very easily caught.
no, they don’t have agi, but this benchmark isn’t proof of that either way, an ai that’s dumber than a human could still be agi
Ii can thoroughly recommend “A Brief History of Intelligence” (by Max Bennett), which explains how intelligence has taken steps through evolution, what those steps were etc.
Spatial intelligence requires spatial understanding and it’s not something that can be solved through a large language model, IMHO.
I’m excited to see how these are solved. And I’m terrified to see how these will be solved.
“Leaderboard” where rank one scores 0.3% success lol
The humans literally didn’t score 100% though. Why lie?
You can really only judge fairness of the score if you understand the scoring criteria. It is a relative score where the baseline is 100% for humans – i.e. A task was only included in the challenge if at least two people in the panel of humans were able to solve it completely, and their action count is a measure of efficiency. This is the baseline used as a point of comparison.
From the Technical Report:
The procedure can be summarized as follows:
• “Score the AI test taker by its per-level action efficiency” - For each level that the test taker completes, count the number of actions that it took.
• “As compared to human baseline” - For each level that is counted, compare the AI agent’s action count to a human baseline, which we define as the second-best human action count. Ex: If the second-best human completed a level in only 10 actions, but the AI agent took 100 to complete it, then the AI agent scores (10/100)^2 for that level, which gets reported as 1%. Note that level scoring is calculated using the square of efficiency.
• “Normalized per environment” - Each level is scored in isolation. Each individual level will get a score between 0% (very inefficient) 100% (matches or surpasses human level efficiency). The environment score will be a weighted-average of level score across all levels of that environment.
• “Across all environments” - The total score will be the sum of individual environment scores divided by the total number of environments. This will be a score between 0% and 100%.So the humans “scored 100%” because that is the baseline by definition, and the AIs are evaluated at how close they got to human correctness and efficiency. So a score of 0.26% is 1/0.0026 ~= 385 times less efficient (and correct) compared to humans.
Yes it works 100% of the sometimes. You see how that sounds stupid?
Right. Thank you for this explanation, the percentages seemed out of context. So, the LLM was able to complete some levels?
If you look at the list of tasks, you can see how the 4 frontier models did. Some of them did complete one or two levels of one or two tasks. None of them completed a whole task. Some of the reasoning logs are funny in the replays.
Yes, the LLMs received credit for each level even if they didn’t complete the entire environment.
They have some replays of tasks on their website: https://arcprize.org/tasks
Here’s one where the human completed all 9 levels in 1458 actions, but the LLM completed only one level in 24 actions, then struggled for 190 actions until it timed-out, I guess. The LLM scored 2.8% because of the weighted average, I think. I didn’t take the time to all do the math, and I’m not sure if the replay action count is accurate, but it gives you an idea.
Human: https://arcprize.org/replay/0d461c1c-21e5-4dc8-b263-9922332a6485
LLM: https://arcprize.org/replay/cc821983-3975-4ae4-a70b-e031f6807bb0
John 1.0 and Caroline 1.0 scored 100%
Cave 1.0 scored 1000000% but also force fed the proxy lemons, so it was treated as a failure.
makes title more clickbait
Boring game…
Not the point
Yep
“…specifically crafted to demonstrate tasks that humans complete easily”
Motherfucker, I can’t work out Minesweeper. I got zero fucking chance with your mystery box bloop game.
Just don’t click where the mines are.
AI code is prewritten and is unable to edit that. Humans edit their “code” every second
It’s funny because that means something like freaking Neurosama made by a YouTuber could probably do better at AGI than these multi billion dollar companies due to it being designed so it can modify it’s own code depending on the task given (and at one point, doing so while not directly prompted).
Of course, this makes Neurosama completely useless at work focused tasks outside of coding, because it can and does refuse to do things on purpose.
And that’s exactly why you won’t see AGI coming from any huge business corporation - because they’re trying to make something that replaces workers, rather than something that has no direct purpose.
(Disclaimer - this is not to say Neurosama is AGI in any way, just that it could probably do the tasks much better than the mainstream AIs can, because it has been build with flexibility and adaptability in mind.)
I’m not sure such a general term is factual.
I doubt I can adapt 100%
